Machine Learning Engineer

Roles & Responsibilities:

 Understand and design solutions to Business problems leveraging Data science and

AI

 Should be able to understand and gather the requirement from Stakeholders.

 Propose and execute the solution and present the deliverables to Stakeholders.

 Manage and optimize the deliverables.

 Mentor and Train new team members.

 Develop POCs to enhance teams’ capability.

Skillsets 1:

Strong understanding of Math, Statistics and the theoretical foundations of Statistical & Machine

Learning, Parametric and Non-parametric models.

 Experience training and evaluating ML models. Perform statistical analysis of results

and refine models.

 Strong understanding of advanced data mining techniques, curating, processing and

transforming data to produce sound datasets.

 Strong understanding of the Machine Learning lifecycle - feature engineering,

training, validation, scaling, deployment, scoring, monitoring, and feedback loop.

 Understanding of NLP techniques for text representation, semantic extraction

techniques, data structures, and modelling.

 Use various statistical techniques and ML methods to perform predictive

modelling/classification for problems around client, distribution, sales, client profiles,

segmentation and provide relevant & actionable recommendations/insights for the

business.

 Stronghold on Visualization tool (or libraries) like SAS Visual Analytics, Tableau,

Python, etc.

 Expert in working on Statistical tools (Python/R/SAS).

 Experience with cloud computing infrastructure like AWS/Azure/GCP.

 Able to develop, test and deploy models on Cloud/Web.

Skillsets 2:

1.BERT and Transformer Models: Proficiency in working with BERT and other transformer-based

models for various NLP tasks, such as text classification, named entity recognition, question

answering, and text generation. Familiarity with fine-tuning BERT on specific downstream tasks using techniques like transfer learning and domain adaptation.

2.Natural Language Processing (NLP): Strong understanding of NLP techniques, including text

preprocessing, information extraction, text representation, sentiment analysis, named entity

recognition, topic modeling, and information retrieval.

3.Machine Learning and Deep Learning: Proficiency in machine learning and deep learning algorithms

and frameworks, such as TensorFlow, PyTorch, scikit-learn, or Keras. Ability to train and evaluate NLP models using supervised and unsupervised learning techniques.

4.Programming Languages: Proficiency in programming languages commonly used in NLP, such as

Python, Java, or C++. Ability to write efficient and clean code for data manipulation, model

development, and deployment.

5.NLP Libraries and Tools: Familiarity with popular NLP libraries and tools, such as NLTK, SpaCy,

Gensim, Stanford NLP, or CoreNLP. Ability to leverage existing librariesfor various NLP tasks and adaptthem to specific use cases.

8.Continuous Learning: Proactive approach to staying updated with the latest advancements in NLP

research, technologies, and best practices. Active participation in conferences, workshops, and online forums.

Qualifications

 MCA/BE/B. Tech. /MBA/M.Stat./M.sc. or equivalent degree in Quant. from reputed

institute.

 3+ years of relevant experience.

Soft Skills:

 Excellent organizational skills and ability to prioritize wide range of tasks

 Demonstrated initiative and creativity - ability to influence (add value)

 Strong interpersonal, communication, organizational and planning skills.